Stoichiometric Representation of Gene–Protein–Reaction Associations Leverages Constraint-Based Analysis from Reaction to Gene-Level Phenotype Prediction

Abstract

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.

Genome‐wide characterization of Phytophthora infestans metabolism: a systems biology approach

“…The relation of a gene to an enzyme and its associated reactions is called the gene–protein–reaction (GPR) association (Machado et al ., ; Thiele and Palsson, ). A reaction can be associated with multiple enzymes (isozymes) and genes (paralogues).…”

Genome-wide characterization of Phytophthora infestans metabolism: a systems biology approach

“…Another proposed metabolite is 243 tyramine that would, upon import in the model, enable the production of six other 244 metabolites. Tyramine is a product of decarboxylation of tyrosine, and based on the 245 genome annotation P. infestans seems to lack the enzyme that catalyzes this reaction The relation of a gene to an enzyme and its associated reactions is called the 265 gene-protein-reaction (GPR) association (Machado et al, 2016;Thiele and Palsson, 266 2010). A reaction can be associated to multiple enzymes (isozymes) and genes 267 (paralogs).…”

Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models

“…FALCON is not the first framework to incorporate enzyme activities as continuous variables in constraint-based models [20,21]. It is the first approach that fully couples activity to the corresponding reaction fluxes.…”

“…To consider enzymes as continuous values in a model, multiple quasi-stoichiometric frameworks have been developed [20,21]. These frameworks rewrite all reactions in a model to consume a pseudo-metabolite representing the activity of the associated enzyme ( Figure 2).…”

“…Using binary variable to restrict fluxes is a common approach to avoid infeasible loops in metabolic models [23]. The binary variables make FALCON models a non-convex MILP, as opposed to other convex LP formulations used to link enzymes and reactions [20,21]. The added complexity of FALCON models is necessary to identify flux coupled reactions.…”